Applied Scientist

Overview

As an Applied Scientist, you will develop scalable machine learning models and data-driven solutions that power better decisions and customer outcomes across ASOS. You will collaborate with data engineers, ML engineers, product teams and business stakeholders to deploy models at scale and influence the scientific direction of our ML capabilities. This role combines research, experimentation and production delivery to create real business impact in a fast-paced retail environment. You will work on challenging problems, contributing to best practices and driving value across domains.

Pay / Benefits

• employee discount
• employee sample sales
• 25 days paid annual leave
• private medical care
• Fixed Annual Payment in addition to salary
• personalised learning opportunities

Responsibilities

• Design, develop and deploy ML models and data-driven solutions in production environments
• Apply ML and optimisation techniques to solve complex business problems
• Partner with engineers to productionise models and build reliable, scalable ML systems
• Design and analyse experiments and evaluation frameworks to measure model performance and business impact
• Explore, evaluate and prototype new approaches from industry and academia
• Work closely with product and business stakeholders to identify opportunities where ML can create value
• Contribute to the team's technical and scientific direction through knowledge sharing, code reviews and collaboration
• Help shape best practices in ML, experimentation and applied research across the organisation

Key requirements

• Developing and deploying ML models in production environments
• Applying statistics, analytics and ML techniques to solve complex business problems
• Experience in developing ML solutions to solve complex problems
• Building predictive models or intelligent systems using large-scale data
• Translating research into production-ready solutions
• Designing and evaluating models using performance, business and customer impact measures
• End-to-end ML lifecycle experience from problem definition to deployment and monitoring
• Applying quantitative, statistical or optimisation techniques to support decision-making and product development
• Proficiency in Python and ML frameworks (PyTorch, TensorFlow)
• Experience with large datasets and distributed data processing
• Strong software engineering practices (testing, version control, maintainable code)
• Ability to communicate technical concepts to technical and non-technical audiences
• Curiosity, pragmatism and willingness to learn, experiment and share knowledge
• Experience bringing ML products from ideation to production
• Experience in fast-paced, product-driven environments
• Familiarity with cloud-native ML platforms and MLOps practices
• Publications, open-source contributions or current knowledge in ML/AI

• Strong communication to both technical and non-technical audiences
• Curiosity and willingness to learn
• Pragmatism and result-orientation
• Python proficiency
• PyTorch or TensorFlow
• Experience with large-scale data and distributed processing

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